1. Field Of The Invention
The present invention relates generally to expert systems, and more particularly to expert systems for diagnosing data communication networks.
2. Related Art
There are at least three types of network problems that occur in data communication networks: physical problems, connectivity problems, and configuration problems.
Physical problems include problems which induce noise on the network, or which physically and/or electrically impede communication on the network. For example, physical problems include defective or incorrectly installed cables and connectors. Physical problems also include cables which are too long or which are broken.
Connectivity problems include problems with spanning devices. For example, connectivity problems include malfunctioning and incorrectly installed repeaters, bridges, and routers.
Configuration problems include problems with configuring and programming devices on the network. For example, a configuration problem occurs when multiple devices are programmed with the same network address. Another configuration problem occurs when a device is programmed with an incorrect broadcast address.
Tools are used to diagnose data communication networks to identify network problems. Protocol analyzers are such tools.
With the tools, human operators may manually diagnose data communication networks. For example, operators may use protocol analyzers to statistically monitor the data communication networks to measure traffic levels, including broadcast traffic levels, and to detect collisions and errors.
Based on network information acquired through using the tools, operators may identify network problems. The operators may correct the network problems once such network problems are identified.
However, there are problems with manually diagnosing data communication networks. For example, novice operators may not have the knowledge and experience to differentiate between important and superfluous information. Thus, novice operators may collect large amounts of unneeded information. Also, novice operators may lack sufficient knowledge and experience to effectively and efficiently operate the tools. Further, novice operators may lack sufficient knowledge and experience to accurately and effectively use network information to detect network problems. Expert operators having sufficient knowledge and experience to effectively, accurately, and efficiently collect data, operate the tools, and detect network problems may not be available to diagnose the data communication networks.
Further, manual diagnosis of data communication networks may result in sporadic monitoring of the networks since such manual diagnosis can be performed only when a human operator is available. Thus, even if expert operators are available, manual diagnosis is not conducive for periodic diagnosis of data communication networks.
A prior solution to the above manual diagnosis problem is to use known expert systems to automatically diagnose data communication networks. Such known expert systems automatically control the tools (such as protocol analyzers) to collect network data. Based on the network data, the known expert systems automatically identify network problems.
However, there are problems with using known expert systems to automatically diagnose data communication networks. First, known expert systems, like novice operators, often collect large amounts of unneeded information.
Second, known expert systems often analyze and interpret the collected data in an inefficient and ineffective manner.
Third, known expert systems do not allow for human operator interaction and control. With known expert systems, operators send commands to initiate the known expert systems. However, once initiated, the known expert systems execute until they complete the operators' commands. The operators do not interact with the known expert systems (once the expert systems are initiated) because the known expert systems often do not provide operators with status information. The operators do not control the known expert systems because, once initiated, the known expert systems do not respond to operator commands.
Providing such operator interaction and control is important for both novice operators and expert operators.
For novice operators, such interaction and control is important for learning purposes. By providing novice operators with interaction and control, novice operators will learn how to control the tools and how to identify network problems.
For expert operators, such interaction and control is important for greater accuracy in the diagnosis process. By providing expert operators with interaction and control, expert operators may draw on their experience and knowledge to either confirm or modify the expert systems' operation. Such confirmation and modification will ultimately result in a more accurate and effective diagnosis process.
Therefore, an expert system for automatically diagnosing data communication networks, which efficiently and effectively collects, analyzes, and interprets data, and which allows for human operator interaction and control, is required.